Development of an AI system for customer churn prediction with action recommendations
Churn prediction without recommendations is half the battle. Knowing that a client might churn isn't enough; you need to know what to do specifically with that client, with that risk profile, with that history.
Prediction model
Survival Analysis: Cox Proportional Hazards and Kaplan-Meier predict the time to a crash, not just probabilities. They answer "when," not just "whether it's likely."
Gradient Boosting Classification: XGBoost/LightGBM for 30/60/90-day churn probability. SHAP values for explainability: "This customer is high risk because: -45% usage over the last 30 days, 2 unresolved support tickets, NPS score of 5."
Feature Engineering: Usage patterns (daily/weekly/monthly active), feature adoption breadth (how many features it uses), login recency, support interaction, email engagement (opens, clicks), invoice status.
Recommendation Engine
For each client at risk, we provide personalized recommendations based on:
- Risk causes (SHAP explanation → action mapping)
- Client segment (Enterprise → exec call; SMB → email sequence)
- Interaction histories (already tried a discount → a different approach)
- Successful retention cases with similar clients
A/B testing of interventions
Different playbooks are tested across segments. Tracking: treatment vs. control group. Retention lift measurement.